Lithium mica flotation collector reaction temperature accurate control method and system

Abstract

The application discloses a kind of lithium mica flotation collector reaction temperature accurate control method and system, belong to automatic control technical field, it includes obtaining real-time process parameters, real-time process parameters include measured temperature, collector addition flow and slurry flow;Real-time process parameters are handled, and virtual reaction temperature field is generated;Virtual reaction temperature field is compared with target reaction temperature, and temperature adjustment decision is generated;According to the local temperature deviation indicated in temperature adjustment decision, the addition parameter of corresponding collector micro-reaction addition unit is adjusted, and temperature compensation is executed by changing the heat release or absorption of collector reaction in local area;Synchronous adjustment heat exchange route between process fluids in cascade heat exchange network, dynamically balance overall heat of system.The application adopts virtual reaction temperature field online sensing and decision-making, combined with local micro-unit compensation and global heat exchange collaborative adjustment, and can realize the collaborative control of collector reaction temperature.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and in particular to a method and system for precise control of the reaction temperature of a lithium mica flotation collector. Background Technology

[0002] Lepidolite, a crucial strategic resource for lithium extraction, relies heavily on flotation processes for its development and utilization. In flotation, the collector, as a key reagent, plays a crucial role in separating the target mineral from the gangue through selective adsorption on the surface of mineral particles. Extensive research and production practice have shown that the chemical reaction process of the collector is extremely sensitive to temperature. The reaction temperature not only directly affects the reaction rate and reagent efficacy but is also one of the key process parameters determining the final concentrate grade and recovery rate. Therefore, stable and precise control of the collector's reaction temperature during flotation is a prerequisite and guarantee for achieving efficient lepidolite flotation.

[0003] In related technologies, Chinese invention patent CN117539298A discloses a method and related equipment for controlling the reaction temperature in a loop polypropylene unit, including: determining the heat load of the loop polypropylene unit, and obtaining the jacket water temperature and loop reactor temperature setpoints; using the loop reactor temperature setpoints as operating variables, using the heat load and jacket water temperature as feedforward variables, adjusting the corresponding loop reactor temperature; using the adjusted loop reactor temperature as the main loop temperature in the temperature cascade loop, and using the jacket water temperature as the secondary loop temperature to adjust the reaction temperature of the loop polypropylene unit.

[0004] However, the aforementioned existing technical solutions have the following technical drawbacks. Existing solutions rely on direct temperature measurement at single or multiple points. This measurement method lacks spatial representativeness and cannot comprehensively and accurately perceive the true distribution of reaction temperature in three-dimensional space when the collector reacts chemically on the surface of mineral particles inside the flotation cell. Therefore, the control basis is a macroscopic, averaged temperature value, rather than the true state of the microscopic reaction region. Existing control methods mainly rely on adjusting the input power of external heat or cold sources for macroscopic temperature regulation. This "overall heating or cooling" method has a slow response speed, low energy utilization efficiency, and large-scale temperature intervention can easily cause unnecessary interference to the local, ideal chemical reaction microenvironment within the flotation cell, affecting process stability. Existing solutions are based on a feedforward-feedback control system with fixed models or parameters, belonging to open-loop or simple closed-loop control. It cannot perform correlation analysis and learning between the final production result and the temperature state during the production process, therefore lacking the ability to self-optimize based on long-term production data and actual results, and is difficult to adapt to changing operating conditions such as fluctuations in ore properties. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a method and system for precise control of the reaction temperature of the collector in lithium mica flotation. By employing online sensing and decision-making of the virtual reaction temperature field, combined with local micro-unit compensation and global heat exchange coordinated adjustment, the collector reaction temperature can be achieved through coordinated control.

[0006] The above objectives can be achieved through the following approach: A method and system for precise control of the reaction temperature of a collector in lepidolite flotation includes: acquiring real-time process parameters, including measured temperature, collector addition flow rate, and slurry flow rate; processing the real-time process parameters to generate a virtual reaction temperature field; comparing the virtual reaction temperature field with the target reaction temperature to generate a temperature adjustment decision; adjusting the addition parameters of the corresponding collector micro-reaction addition unit according to the local temperature deviation indicated in the temperature adjustment decision, and performing temperature compensation by changing the heat release or absorption of the collector reaction in the local area; and synchronously adjusting the heat exchange routes between process fluids in the cascaded heat exchange network to dynamically balance the overall heat of the system.

[0007] Optionally, generating a virtual reaction temperature field characterizing the reaction state of the collector on the surface of mineral particles includes: acquiring ore characteristic parameters of the ore's physicochemical properties; fusing the real-time process parameters with the ore characteristic parameters to form a comprehensive process state vector; inputting the comprehensive process state vector into a preset thermodynamic-fluid dynamics model for calculation, and outputting a virtual reaction temperature field containing information on the spatial temperature distribution of the reaction zone within the flotation cell.

[0008] Optionally, adjusting the addition parameters of the corresponding collector microreaction addition unit includes: identifying a first local region below the target reaction temperature indicated by the temperature adjustment decision; determining a target microreaction addition unit serving the first local region based on the mapping relationship between the three-dimensional space within the flotation cell and the execution unit; generating and sending a first control command to the target microreaction addition unit according to the compensation amount required by the temperature adjustment decision, increasing its collector addition concentration or preheating the added collector, thereby heating the first local region in situ through enhanced chemical reaction heat.

[0009] Optionally, adjusting the addition parameters of the corresponding collector microreaction addition unit further includes: identifying a second local region indicated by the temperature adjustment decision that is higher than the target reaction temperature; determining the corresponding target microreaction addition unit serving the second local region based on the mapping relationship between the three-dimensional space in the flotation cell and the execution unit; generating and sending a second control command to the corresponding target microreaction addition unit according to the compensation amount required by the temperature adjustment decision, reducing its collector addition concentration or cooling the added collector, thereby reducing the heat of chemical reaction to perform in-situ cooling of the second local region.

[0010] Optionally, the heat exchange routing between process fluids in the synchronously regulated cascaded heat exchange network includes: acquiring the current temperature and flow rate of each process fluid in the cascaded heat exchange network; calculating and generating an optimized heat exchange routing instruction based on the virtual reaction temperature field and the current temperature and flow rate of each process fluid using a preset energy routing optimization algorithm; and dynamically adjusting the valve opening of each heat exchange branch in the cascaded heat exchange network according to the optimized heat exchange routing instruction, so as to directionally transport heat from the heat-rich fluid to the heat-demanding fluid.

[0011] Optionally, the method further includes: after the completion of the lithium mica flotation production batch, obtaining the final flotation index of the production batch; performing correlation analysis between the final flotation index and the historical virtual reaction temperature field data of the production batch during the production process to generate model optimization results; and adjusting the internal parameters of the thermodynamic-fluid dynamics model based on the model optimization results.

[0012] Optionally, the step of comparing the virtual reaction temperature field with a preset target reaction temperature to generate a temperature regulation decision includes: comparing the temperature of each grid node in the virtual reaction temperature field with the target reaction temperature point by point, and calculating the temperature deviation value of each node; based on the temperature deviation value and a preset temperature deviation threshold, identifying a first local region below the target reaction temperature and a second local region above the target reaction temperature, and determining the spatial location and deviation amount of each region; and generating a temperature regulation decision that includes region attributes and compensation amounts based on the type, spatial location, and deviation amount of the first and second local regions.

[0013] Optionally, the generation of temperature regulation decision further includes: if the temperature regulation decision indicates an overall temperature deviation, generating an external energy regulation command; and adjusting the input power of an external heat source or an external cold source according to the external energy regulation command.

[0014] Optionally, the acquisition of real-time process parameters includes: collecting measured temperatures, collector addition flow rates, and slurry flow rates at different locations of each point; and performing noise reduction, filtering, and time synchronization calibration on the collected raw data to form standardized real-time process parameters.

[0015] Based on the same inventive concept, this invention also provides a precise control system for the reaction temperature of the collector in lepidolite flotation. The system includes: a process parameter acquisition module for acquiring real-time process parameters, including measured temperatures at different locations, collector addition flow rates, and slurry flow rates; a virtual reaction temperature field generation module for processing the real-time process parameters to generate a virtual reaction temperature field characterizing the reaction state of the collector on the surface of mineral particles; a temperature regulation decision module for comparing the virtual reaction temperature field with a preset target reaction temperature to generate a temperature regulation decision; a collector micro-reaction addition unit control module for adjusting the addition parameters of the corresponding collector micro-reaction addition unit according to the local temperature deviation indicated in the temperature regulation decision, thereby performing temperature compensation by changing the heat release or absorption of the collector reaction in the local area; and a cascaded heat exchange network control module for synchronously adjusting the heat exchange routes between process fluids in the cascaded heat exchange network to dynamically balance the overall system heat.

[0016] Compared with the prior art, the present invention has the following advantages: This invention achieves full-space, high-precision perception of the reaction state inside the flotation cell by constructing a virtual reaction temperature field. It breaks through the limitations of traditional single-point or multi-point temperature measurement in terms of spatial representativeness, enabling temperature control to be based on a deeper understanding of the micro-reaction region from macroscopic average values, thus improving the accuracy and targeting of the control.

[0017] This invention proposes a two-level collaborative control strategy that combines local in-situ compensation with global heat balance. By adjusting the collector micro-reaction addition unit, rapid and precise local temperature compensation is achieved using the heat of chemical reaction, resulting in fast response and direct intervention. Simultaneously, a cascaded heat exchange network is linked for systematic heat scheduling, ensuring the overall thermal stability and energy utilization efficiency of the process.

[0018] This invention establishes a self-optimizing closed-loop feedback mechanism based on actual production results. By correlating the final flotation index with historical virtual reaction temperature field data during the production process, the core thermodynamic-fluid dynamics model can be continuously iteratively optimized, enabling the control system to have adaptive learning capabilities. This allows it to adapt to changes in operating conditions such as fluctuations in ore properties, ensuring the long-term effectiveness and robustness of the control strategy.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart of a method for precisely controlling the reaction temperature of a lithium mica flotation collector according to an embodiment of the present invention.

[0022] Figure 2 This is a spatial distribution diagram of the virtual reaction temperature field in an embodiment of the present invention.

[0023] Figure 3 This is a control response diagram of the local micro-reaction addition unit in an embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of the structure of a precise control system for the reaction temperature of a lithium mica flotation collector according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Reference Figure 1 One embodiment of the present invention proposes a method for precise control of the reaction temperature of the collector in lithium mica flotation. The method employs online sensing and decision-making of the virtual reaction temperature field, combined with local micro-unit compensation and global heat exchange coordinated adjustment, to achieve coordinated control of the collector reaction temperature.

[0027] The method described in this embodiment specifically includes: S1. Obtain real-time process parameters, including measured temperature at different locations, collector addition flow rate, and slurry flow rate; Optionally, obtaining real-time process parameters includes: The measured temperature, collector addition flow rate, and slurry flow rate were collected at different locations at each point; The collected raw data is denoised, filtered, and time-synchronized to form standardized real-time process parameters.

[0028] Specifically, the real-time acquisition of multi-source heterogeneous data aims to capture raw electrical signals describing the flotation process from the physical world. This step is accomplished through various sensor arrays deployed at the process site. Specifically, measured temperatures at different locations are obtained using armored thermocouples or PT100 platinum resistance temperature sensors distributed at the flotation cell inlet, outlet, different depths, and near the wall; collector addition flow rate is measured using high-precision Coriolis mass flow meters or miniature electromagnetic flow meters installed on the pipelines of each micro-reaction addition unit, typically with a range of 0.1 to 5 liters per hour; and slurry flow rate is monitored by a large-diameter electromagnetic flow meter installed on the main feed pipeline. The data acquisition module continuously polls all hardware interfaces at a preset high-frequency period, such as 10 Hz, converting the analog electrical signals output by the sensors into unprocessed raw data streams via A / D converters, and adding an acquisition timestamp.

[0029] The system performs a series of standardization processes on the collected raw data to eliminate errors introduced by sensor noise, electromagnetic interference in the industrial environment, and transmission delays between different data channels. This refines the raw data into standardized real-time process parameters that are directly usable by the model and accurately reflect the true dynamics of the physical process. This processing flow includes several sequential algorithmic steps. The first step is noise reduction and filtering. For continuously changing signals such as temperature and flow rate, the system uses digital low-pass filters, such as Kalman filters or moving average filters, to smooth the data curves and filter out random spikes and high-frequency noise caused by motor start-stop or pump pulsation. The second step is crucial time synchronization calibration. Because different sensors have different response times, signal transmission path lengths, and data bus communication protocols, directly using raw data can lead to slight misalignments in timestamps for various parameters, which is fatal for dynamic models that require instantaneous state snapshots. Therefore, the system adopts a global time synchronization mechanism based on a network time protocol and establishes a dynamic time window buffer in the data processing center. Based on a unified timestamp accurate to the millisecond level, data from different channels is aligned and interpolated to ensure that each set of output data points represents the system state at the same physical moment. After this series of processing steps, the system finally outputs a set of structured, standardized real-time process parameter vectors that are completely synchronized across the cross-section at the same time, providing a clean and reliable data foundation for the subsequent generation of the virtual reaction temperature field.

[0030] For example, the process parameter acquisition module performs real-time acquisition of multi-source heterogeneous data, acquiring raw electrical signals from the physical world through PT100 platinum resistance temperature sensors deployed at different depths in the flotation cell and Coriolis mass flow meters on the pipeline. The system continuously polls the hardware interface at a high frequency of 10Hz, converting the analog signals output by the sensors into raw data streams via an A / D converter. For the acquired raw temperature data of 45.23℃ and flow rate data of 2.58L / h, the system first applies a digital low-pass filter for noise reduction and filtering, smoothing out random spikes caused by motor start-stop. The smoothed signal after noise filtering can more accurately reflect physical dynamics. Subsequently, the system performs critical time synchronization calibration. Due to the different response times and transmission paths of the temperature sensor and flow meter, the system adopts a global time synchronization mechanism based on a network time protocol, establishing a dynamic time window buffer in the data processing center to uniformly align the data of each channel to millisecond-level timestamps. A linear interpolation algorithm is used to correct asynchronous data points, ensuring that each output parameter vector is completely synchronized on the same cross-section at the same time. After the above standardization process, the system generates a set of accurate standardized real-time process parameters, providing a clean and reliable data foundation for the subsequent construction of the virtual reaction temperature field.

[0031] S2. Process the real-time process parameters to generate a virtual reaction temperature field characterizing the reaction state of the collector on the surface of mineral particles; Optionally, the generation of the virtual reaction temperature field characterizing the reaction state of the collector on the surface of mineral particles includes: Obtain ore characteristic parameters for the physicochemical properties of the ore; The real-time process parameters are fused with the ore characteristic parameters to form a comprehensive process state vector; The integrated process state vector is input into a preset thermodynamic-fluid dynamics model for calculation, and a virtual reaction temperature field containing spatial temperature distribution information of the reaction zone within the flotation cell is output.

[0032] Specifically, obtaining ore characteristic parameters aims to provide static physicochemical baseline data on the material side for the model. These ore characteristic parameters are fundamental to the thermodynamic and kinetic behavior of the collector reaction. Specific parameters obtained include, but are not limited to, the grade of lepidolite ore. The mass fraction of the minerals, typically between 1.5% and 4.5%; the degree of liberation of the minerals; the particle size distribution of the ore, especially the percentage of -200 mesh particles; and the specific surface area of ​​the minerals. These parameters are usually determined offline using laboratory particle size analysis, chemical composition analysis, and X-ray diffraction analysis before the start of each production batch, and are then input into the control system as a set of static data.

[0033] The purpose of constructing the integrated process state vector is to integrate static and dynamic data describing the current operating state of the system into a standardized input format recognizable by the model. This step fuses real-time process parameters with the aforementioned ore characteristic parameters. The integrated process state vector is a multi-dimensional array containing a complete snapshot of the system at the current moment. Real-time process parameters such as collector addition flow rate, slurry flow rate, flotation cell agitator speed, and measured temperatures at key locations are continuously collected and combined with pre-input ore characteristic parameters to form this vector. For example, an integrated process state vector... It can be represented as: , in, Represents ore grade. The median grain size of the ore. Specific surface area For the collector flow rate, For slurry flow rate, The speed of the stirrer. For the first Measured temperatures at each observation point. Ore grade, Median grain size of ore The specific surface area of ​​the ore is a characteristic parameter of the ore. It is obtained by sampling the feed ore before each production batch and by laboratory chemical or X-ray fluorescence analysis, laser particle size analysis, and BET specific surface area analysis. The collector addition flow rate is collected online in real time via a Coriolis mass flow meter or a miniature electromagnetic flow meter on the collector micro-reaction addition unit pipeline. The slurry flow rate is collected online in real time by a large-diameter electromagnetic flow meter on the main feed pipeline. The agitator speed is read online from the frequency converter control unit or motor drive system of the flotation cell agitator in real time. No. The measured temperature at each observation point is acquired online in real time using armored thermocouples or PT100 platinum resistance temperature sensors distributed throughout the flotation cell. , , These are batch static parameters, measured only once. , , , These are dynamic process parameters, all of which are acquired or read in real time at high frequency cycles.

[0034] The calculations are performed using a pre-defined thermodynamic-fluid dynamics model to solve the complex coupling problem of multiphase flow and temperature fields within the flotation cell, ultimately outputting a virtual reaction temperature field. The model takes a comprehensive process state vector as both boundary and initial conditions. It integrates two sets of core physical equations. The first part is the Navier-Stokes equations based on computational fluid dynamics, used to simulate the macroscopic flow, turbulent mixing, and suspension distribution of mineral particles under the action of an agitator. The second part is the energy conservation equations, used to calculate heat transfer and generation within the system; its general form can be expressed as: , In the formula, This is the slurry density, the value of which is calculated from the real-time slurry concentration; Specific heat capacity of the slurry; Temperature is the objective variable to be solved. For time; This is the slurry velocity field vector calculated from the fluid dynamics section; The turbulence effect of the slurry was considered for the effective thermal conductivity. This is the heat source term. Here... This is the key element of the model, integrating the heat of adsorption reaction between the collector and the surface of lepidolite particles, the heat dissipation from the mechanical energy consumption of the stirrer converted into viscous heat dissipation, and the heat exchange with the external environment. The calculation of the heat of reaction is strongly correlated with the ore characteristics and process parameters in the integrated process state vector. By solving the above coupled equations on a discretized three-dimensional mesh, the model ultimately outputs a three-dimensional data matrix. Each element of this matrix corresponds to the predicted temperature value of a spatial grid point within the flotation cell, collectively forming a high-resolution virtual reaction temperature field. The spatial distribution of the virtual reaction temperature field is as follows: Figure 2 As shown, this temperature field not only reflects the overall temperature level, but more importantly, it reveals local hot spots or cold zones caused by uneven reaction and insufficient mixing, providing a data foundation for achieving localized and precise temperature compensation.

[0035] For example, before the start of a lithium mica flotation production batch, ore characteristic parameters, including ore grade, are measured through laboratory analysis. The median particle size of the ore was 3.0%. 75 Specific surface area of ​​ore It is 1.2 During production, the process parameter acquisition module acquires dynamic data in real time at a frequency of 10Hz. The collector addition flow rate is set at a specific instant. It is 2.5 slurry flow rate 150 Stirrer speed 280 At this time, the measured temperatures at three observation points located at different positions in the flotation cell were... The temperatures are 44.2℃, 44.5℃, and 44.8℃, respectively. These static and dynamic parameters are then combined to form the comprehensive process state vector for that moment. This vector serves as the input boundary of the thermodynamic-fluid dynamics model, providing the data foundation for full-field temperature calculations. The system invokes the preset thermodynamic-fluid dynamics model to solve the energy conservation equations on a discretized three-dimensional grid. Taking one grid node as an example, the calculation is performed, and the slurry density is determined based on the real-time slurry concentration. 1200 Specific heat capacity of slurry 3800 The fluid dynamics section calculates the slurry velocity field vector at that point. Components, and combined with effective thermal conductivity Addressing heat transfer components. Key heat source components. The heat generated consists of the heat of adsorption reaction of the collector on the surface of lepidolite, the heat dissipation due to the conversion of mechanical energy consumption into viscous heat dissipation, and the heat exchange with the environment. If the exothermic reaction rate between the collector and the mineral at this node is calculated to be 500... The dissipation from stirring is 50. The ambient heat dissipation is -10. ,but The model obtains the predicted temperature of the grid node through iterative solutions. The temperature is 45.2℃. The final output is a three-dimensional data matrix containing the temperature values ​​of all spatial grid points in the flotation cell, which is the virtual reaction temperature field, thus revealing the spatial distribution information of local hot spots or cold zones.

[0036] S3. Compare the virtual reaction temperature field with the preset target reaction temperature to generate a temperature regulation decision; Optionally, comparing the virtual reaction temperature field with a preset target reaction temperature to generate a temperature regulation decision includes: The temperature of each grid node in the virtual reaction temperature field is compared with the target reaction temperature point by point, and the temperature deviation value of each node is calculated. Based on the temperature deviation value and the preset temperature deviation threshold, a first local region below the target reaction temperature and a second local region above the target reaction temperature are identified, and the spatial location and deviation amount of each region are determined. Based on the type, spatial location, and deviation of the first and second local regions, a temperature regulation decision containing regional attributes and compensation amounts is generated.

[0037] Specifically, the calculation of the temperature deviation field aims to transform the raw temperature information into deviation information that directly reflects the control error, providing a foundation for subsequent analysis. The control system traverses each three-dimensional spatial grid node in the virtual response temperature field output by the model. For any node with coordinates... The system performs point-by-point comparisons at each location and calculates the temperature deviation value. The calculation formula is: , In the formula, This is the virtual reaction temperature at that node, calculated using a thermodynamic-fluid dynamics model; This is the preset, globally unified target reaction temperature for the entire flotation process, such as 45 degrees Celsius. After the calculation is completed, the system generates a three-dimensional temperature deviation field matrix with the same dimension as the virtual reaction temperature field. This matrix intuitively represents the degree of "coldness" and "heat" at various points within the flotation cell.

[0038] The purpose of identifying and quantifying anomalous regions based on temperature deviation fields is to identify continuous spatial regions with practical engineering significance that require intervention from massive amounts of node data. The system first applies a preset temperature deviation threshold. This threshold is typically set between 0.2 and 0.5 degrees Celsius to filter out minor deviations that do not require a response, caused by model calculation errors or normal process fluctuations. Then, the system scans the entire temperature deviation field and uses image processing algorithms such as connected component analysis to cluster adjacent grid nodes that meet specific conditions into regions. Specifically, all nodes that meet... Connected nodes are identified and marked as the first local region; all satisfying Connected nodes are identified and marked as second local regions. For each identified region, the system further calculates its key attributes, including spatial location, geometric volume, and average temperature deviation.

[0039] Based on the identified regional attributes, a structured temperature regulation decision is generated. The aim is to package the analysis results into a standardized data object for direct use by downstream control modules. This decision is a list containing multiple entries, each corresponding to an identified anomalous region. Each entry contains the following fields: regional attributes, explicitly indicating whether the region is a first or second local region; spatial location, providing the region's three-dimensional coordinates; and compensation amount, a calculated key parameter guiding the intensity of subsequent control actions. This compensation amount is typically not a simple temperature difference, but rather the power to be compensated. The calculation method is as follows: , In the formula, It is the volume of the region; It is the density of the slurry; It is the specific heat capacity of the slurry; This is the average temperature deviation in the region; This is a desired corrected time constant, reflecting the expected response speed of the control system. This generated temperature regulation decision, incorporating information from all regions, provides precise input for the targeted regulation of subsequent collector microreaction addition units and the global scheduling of the cascaded heat exchange network.

[0040] For example, the temperature control decision module scans the virtual reaction temperature field point by point. It sets the target reaction temperature preset by the process. The system identified a virtual temperature of a grid node in a certain spatial region, at 45.0℃. All were 44.2℃. System application formula. The calculations were performed to obtain the temperature deviation values ​​for each node in the region. The temperature deviation is -0.8℃. Since the absolute value of this deviation is greater than the preset temperature deviation threshold of 0.3℃, the system classifies this connected region as the first local region. The volume of this region is known. It is 0.5 Real-time slurry density 1200 Specific heat capacity of slurry 3800 Expected correction time Set to 60 seconds. Substitute... Ultimately, the system will calculate the compensation amount. Encapsulated in the temperature regulation decision instruction, it is sent to the underlying execution module along with the spatial coordinate mapping information of the first local area.

[0041] Optionally, the generation of temperature regulation decisions further includes: If there is an overall temperature deviation in the temperature regulation decision indication, an external energy regulation command is generated; Adjust the input power of the external heat source or external cold source according to the external energy adjustment command.

[0042] Specifically, during the overall temperature deviation state diagnosis, the control logic continuously evaluates the abnormal spatial distribution data included in the temperature regulation decision. When the system detects that more than 70% to 80% of the effective volume of the flotation cell deviates from the target reaction temperature in the same direction, and the deviation between the global average temperature and the target value exceeds the preset macroscopic alarm dead zone threshold, such as 1.5 degrees Celsius, the system diagnoses that the current operating condition faces an overall temperature deviation that cannot be resolved by internal balancing. This determination directly triggers the external energy dispatch mechanism. Next, the system calculates and issues external energy regulation commands to determine the accurate energy injection or removal rate required to reverse this macroscopic deviation. The system comprehensively calculates the thermodynamic boundary and outputs the control setpoint that the external equipment should bear. The macroscopic control power solution equation is as follows: , in This indicates the total target adjustment power issued by the control module to the external energy system. The total mass of slurry loaded in the flotation cell in real time is obtained by real-time feedback and conversion from the level radar and density meter. This indicates the specific heat capacity of the multiphase slurry fluid under the current operating conditions of the system. This represents the calculated global average temperature deviation. This represents the expected macroscopic thermal environment recovery time constant of the system, and is typically set between 300 and 800 seconds to avoid excessive overshoot. This refers to the compensation item for the static loss or absorption of heat flow from the tank to the surrounding workshop environment.

[0043] The system executes input power regulation at the physical ports of external heat or cold sources based on the generated external energy regulation commands. If the above deviation reflects an overall overcooling of the system, the underlying PLC controller will... The steam flow rate is converted to a setpoint for the external heat source, and a corresponding analog control signal is output to increase the opening of the proportional regulating valve of the steam pipeline network, accelerating the direct injection of high-enthalpy steam into the slurry layer for heat release. If this deviation reflects an overall overheating state that inhibits the effectiveness of the collector, the bottom-level controller routes the command to the external cold source unit, increasing the operating frequency of the refrigerant circulation pump at the side of the plate cooler proportionally or opening the large-diameter bypass coolant valve to forcefully remove the heat from the main body of the slurry. Through this macroscopic coarse adjustment action, combined with the local fine adjustment action of the collector addition position, the ideal reaction temperature of the lepidolite mineral interface is precisely maintained.

[0044] For example, the control system continuously evaluates abnormal spatial distribution data in temperature regulation decisions during operation. When the system detects that an area exceeding 80% of the effective volume of the flotation cell deviates from the target reaction temperature in the same direction, and the deviation of the global average temperature from the target value exceeds a preset macroscopic alarm dead zone threshold of 1.5℃, it confirms the existence of an overall temperature deviation in the current operating condition and triggers an external energy dispatch mechanism. The system calculates the required external energy regulation amount through macroscopic control power solving equations. The total mass of slurry currently loaded in the flotation cell is set. The specific heat capacity of the multiphase slurry fluid is 180,000 kg. 3800 The calculated global average temperature deviation The expected macroscopic thermal environment recovery time constant is -2.0℃. Set to 500s, this is the compensation item for the low-noise heat flow loss from the tank to the environment. The value is 5000W. Substituting this into the formula, the target regulating power is calculated. The total target power was thus determined to be 2741000W. The system then generated an external energy regulation command. Since the deviation reflected overall system overcooling, the bottom-level controller converted this power value into a steam flow setpoint for the external heat source and increased the opening of the proportional regulating valve of the steam pipeline network. This was done to reverse the macroscopic deviation by accelerating the injection of high-enthalpy steam into the slurry layer to release heat.

[0045] S4. Based on the local temperature deviation indicated in the temperature regulation decision, adjust the addition parameters of the corresponding collector micro-reaction addition unit, and perform temperature compensation by changing the heat release or absorption of the collector reaction in the local area. Optionally, adjusting the addition parameters of the corresponding collector micro-reaction addition unit includes: Based on the temperature regulation decision, a first local region indicating a temperature below the target reaction temperature is identified; Based on the mapping relationship between the three-dimensional space within the flotation cell and the execution unit, the target micro-reaction addition unit serving the first local region is determined; Based on the compensation amount required by the temperature regulation decision, a first control command is generated and sent to the target micro-reaction addition unit to increase its collector addition concentration or preheat the added collector, thereby heating the first local area in situ through enhanced chemical reaction heat.

[0046] Specifically, the first step involves identifying and locating the local area to precisely pinpoint the spatial range requiring heat compensation. The control system receives and analyzes the temperature regulation decision generated in the previous step. This decision includes all temperature deviation information derived from the virtual reaction temperature field analysis. The system then filters out all spatial grid points that meet the specified conditions. , in It is the virtual reaction temperature of the grid points; It is the preset target reaction temperature, which is preset based on the lithium mica flotation process test and production practice; This is a control dead zone threshold, such as 0.3 degrees Celsius, set to avoid frequent oscillations in the control system, and preset based on process temperature control accuracy and system anti-interference requirements. These grid points together constitute the first local area that needs to be heated, and the three-dimensional coordinate range, volume, and average temperature deviation of this area are recorded.

[0047] The purpose of determining the target microreaction addition unit is to accurately allocate a spatial temperature control requirement to one or more specific physical actuators. Multiple collector microreaction addition units are distributed within the flotation cell, each unit's reagent primarily affecting a specific downstream flow field region. The system internally maintains a pre-defined three-dimensional spatial mapping matrix between the addition units and the execution units. This mapping, established during system commissioning through computational fluid dynamics simulations or tracer experiments, quantifies the influence weight of each addition unit on various three-dimensional grid regions within the flotation cell. Once the first local region is identified, the system queries this mapping matrix and selects the microreaction addition unit or group of microreaction addition units with the highest influence weight on that region as the target microreaction addition unit.

[0048] The system generates and sends a first control command to execute in-situ heating, aiming to calculate the specific actuator adjustment based on the required heat compensation. The system calculates the required target heat input based on the compensation amount defined in the temperature regulation decision, namely the temperature difference and volume of the area to be heated. The instruction includes two selectable or combined control strategies. The first strategy is to increase the concentration of the collector. The system is based on the exothermic enthalpy of the reaction between the collector and lepidolite. The collector concentration or flow rate increment is calculated using the following control law, obtained through calorimetric experiments. : , In the formula, It is a proportional control gain coefficient, obtained through online tuning; It is the volume of the first local region, calculated from the three-dimensional coordinates of the region identified by the virtual reaction temperature field; This is the reaction efficiency coefficient, obtained by calibration based on the on-site operating conditions of the flotation process. The control system will use this increment. The first strategy involves superimposing the existing setpoints of the target microreaction addition unit onto the existing setpoints to generate new concentration or flow rate setpoints, achieved by controlling the metering pump. The second strategy involves preheating the added collector. The system instructs the micro-heater integrated into the target microreaction addition unit to activate and heat the collector to a specific temperature. This temperature setpoint is also based on the required heat compensation. Calculations show that both methods can effectively enhance the release of heat from the chemical reaction or directly introduce heat, providing precise and rapid in-situ heating to the first local region until the virtual reaction temperature field feedback value of that region returns to the target range. The local micro-reaction adds unit control response, such as... Figure 3 As shown.

[0049] For example, the temperature regulation decision module performs a grid point traversal on the virtual reaction temperature field and identifies the conditions that meet the requirements. A set of grid points for the given conditions. A preset target reaction temperature is set. The temperature is 45.0℃, and the dead zone threshold is controlled. The virtual temperature is 0.3℃. If the virtual temperature of a certain connected region... The thermodynamic-fluid dynamic model calculated the temperature to be 44.2℃, thus identifying this region as the first local region below the target temperature. The system records the geometric volume of this region. for Based on the preset mapping matrix between the three-dimensional space of the flotation cell and the execution unit, the No. 1 collector micro-reaction addition unit serving this coordinate range is determined as the target execution device. This is to ensure that the desired correction time is achieved within the specified timeframe. To eliminate the deviation within 60 seconds, the system calculates the required compensation power according to the formula in the instruction manual. Substitute the measured slurry density... 1200 Specific heat capacity of slurry 3800 and average temperature deviation The target heat input is 0.8℃. The value is 30400 W. The exothermic enthalpy of the collector reaction is known. 40000 Reaction efficiency coefficient The proportional control gain coefficient is 0.8. The value is 1.2. The increase in collector concentration is calculated using the control law. It is 2.28 The system then sends a first control command to the No. 1 target micro-reaction addition unit to increase its reagent concentration and perform in-situ heating of the first local area through enhanced chemical reaction heat until the virtual reaction temperature field feedback value returns to the target range.

[0050] Optionally, adjusting the addition parameters of the corresponding collector micro-reaction addition unit further includes: Based on the temperature regulation decision, a second local region indicating a temperature higher than the target reaction temperature is identified; Based on the mapping relationship between the three-dimensional space within the flotation cell and the execution unit, the corresponding target micro-reaction addition unit serving the second local region is determined; Based on the compensation amount required by the temperature regulation decision, a second control command is generated and sent to the corresponding target micro-reaction addition unit to reduce its collector addition concentration or cool the added collector, thereby reducing the heat of chemical reaction and performing in-situ cooling of the second local area.

[0051] Specifically, the identification and demarcation of the second local region aims to precisely pinpoint the spatial area requiring heat suppression. The control system analyzes temperature regulation decisions in real time and filters out all grid nodes in the virtual reaction temperature field that exceed temperature limits based on preset conditions. , in The virtual reaction temperature of the grid points. The target reaction temperature, To control the upper limit of the response, a threshold value of 0.3 to 0.5 degrees Celsius is typically set to ensure control stability. The set of all grid points that meet this condition constitutes the second local region requiring cooling. The system records the three-dimensional spatial coordinates, volume, and average overtemperature amplitude of this region.

[0052] Based on the mapping relationship between the three-dimensional space within the flotation cell and the execution unit, the matching of the target micro-reaction addition unit aims to assign an abstract spatial cooling task to the most efficient physical actuator. This mapping relationship is a pre-calibrated database or model that describes the influence weight of each collector micro-reaction addition unit on the hydrodynamics of different regions within the flotation cell. The system queries this mapping relationship based on the coordinate information of the second local region and selects the micro-reaction addition unit or group of micro-reaction addition units with the highest influence weight for that region as the corresponding target micro-reaction addition unit for performing the in-situ cooling task.

[0053] A second control command is generated and sent to the corresponding target micro-reaction addition unit to implement in-situ cooling. The purpose is to precisely adjust the operating parameters of the execution unit based on the calculated required heat removal amount. The system calculates the target heat to be suppressed or removed based on the compensation amount defined in the temperature regulation decision, namely the overtemperature value and volume of the area to be cooled. The instruction includes two feasible control strategies. The first is to reduce the concentration of the collector. This is based on the exothermic enthalpy of the collector reaction. The reduction in collector concentration or flow rate is calculated using the following negative feedback control law. : , In the formula, It is a proportional negative gain coefficient used to adjust the intensity of the control response, and is obtained by online tuning on-site during the flotation process; It is the volume of the second local region, calculated from the three-dimensional coordinates of the region identified by the virtual reaction temperature field; It is the molar heat effect of the collector reaction; This is the reaction efficiency coefficient. The calculated negative value... A new, lower setpoint will be generated by subtracting from the current setpoint of the target microreactor addition unit. The second strategy is to pre-cool the added collector. The command will activate a miniature cooling device, such as a thermoelectric cooler, on the target microreactor addition unit pipeline to cool the collector to a setpoint below the slurry temperature. This setpoint is also based on the amount of heat to be removed. Dynamic calculation. By reducing the intensity of the heat source of the chemical reaction or directly introducing cooling energy through these two methods, precise and rapid in-situ cooling of the second local region is achieved until the temperature of the region returns to the allowable range of the target reaction temperature.

[0054] For example, the temperature regulation decision module performs a global scan of the real-time generated virtual reaction temperature field and identifies grid point temperatures that meet the requirements. A set of conditions. Setting the target reaction temperature pre-defined for the process. The upper limit threshold for controlling the response is 45.0℃. If the average virtual temperature of the grid points in a connected region is calculated to be 46.2℃ by the model, the system will lock that region as a second local region with a temperature higher than the target temperature, with a target temperature of 0.5℃. The system records the geometric volume of this region. It is 0.4 Based on a pre-defined database of mapping relationships between the three-dimensional space of the flotation cell and the execution unit, the No. 2 collector micro-reaction addition unit, which has the highest weight in influencing the spatial coordinates, is identified as the target unit for performing the in-situ cooling task. To remove excess heat within a preset time, the system first calculates the target heat to be removed based on the temperature deviation field. The heat value is 18240 W. The molar heat effect of this collector reaction is known. 40000 Reaction efficiency coefficient The proportional negative gain coefficient is 0.8. The setting was 1.1 after online tuning. The collector concentration reduction was calculated by solving a negative feedback control law. -1.5675 The system then issued a second control command to the No. 2 target micro-reaction addition unit to reduce its reagent concentration and perform in-situ cooling of the second local area by weakening the intensity of the chemical reaction heat source until the temperature of the area returned to the allowable range of the target reaction temperature.

[0055] S5. Synchronously adjust the heat exchange routes between process fluids in the cascaded heat exchange network to dynamically balance the overall heat of the system.

[0056] Optionally, the heat exchange routes between process fluids in the synchronously regulated cascaded heat exchange network include: Obtain the current temperature and flow rate of each process fluid in the cascaded heat exchange network; Based on the virtual reaction temperature field and the current temperature and flow rate of each process fluid, an optimized heat exchange route instruction is generated by calculating using a preset energy routing optimization algorithm. According to the optimized heat exchange routing instructions, the valve opening of each heat exchange branch in the cascaded heat exchange network is dynamically adjusted to directionally transport heat from the heat-rich fluid to the heat-demanding fluid.

[0057] Specifically, the periodic acquisition of the overall process fluid status is performed to provide real-time and accurate calculation data for the energy routing optimization algorithm. This step is achieved through temperature sensors and flow meters deployed at key nodes of the cascaded heat exchange network. The cascaded heat exchange network is a flexible network consisting of multiple plate or shell-and-tube heat exchangers, pumps, and intelligent regulating valves, allowing different process fluids to exchange heat on demand. The temperature and instantaneous flow rate of each process fluid are collected before it enters and leaves each branch of the network. The data acquisition frequency is typically set between 1 and 5 Hz to ensure rapid response to process fluctuations.

[0058] The energy routing optimization algorithm is used to calculate the optimal heat exchange routing scheme for the current system state. The algorithm's input consists of two parts: one is the real-time temperature and flow rate of each process fluid, which defines the real-time potential of available heat sources and heat sinks in the network; the other is the overall heat demand or surplus state of the flotation system revealed by the virtual reaction temperature location. The objective function of the optimization algorithm is to minimize the total operating cost, which comprehensively considers the consumption of external energy and the penalty term for flotation cell temperature deviation from the target value. The core of the algorithm is to solve a mixed-integer nonlinear programming problem, whose decision variables are the on / off state and flow allocation ratio of each heat exchange branch in the network. After calculation, the algorithm outputs a set of clear optimized heat exchange routing instructions, such as the instruction "Introduce 30% of the flow rate of the high-temperature tailings slurry from route A into heat exchanger No. 3 for heat exchange with the low-temperature grinding slurry from route B". The energy routing optimization algorithm is an improved particle swarm optimization mixed-integer nonlinear programming algorithm adapted to the lepidolite flotation process. It is specifically designed for dynamic optimization of heat exchange routes in cascaded heat exchange networks. The algorithm's objective function is to minimize the total operating cost of heat exchange, which includes three parts: external energy consumption cost, fluid transport energy consumption cost, and temperature deviation penalty cost. It is also constrained by four conditions: energy conservation, branch flow rate of 0.1 to 1.0 times the rated value, fluid temperature fluctuation of ±0.5℃ after slurry heat exchange, collector temperature of 15 to 55℃, and 0-1 matching of heat exchangers. The algorithm input is the real-time fluid temperature of each branch in the cascaded heat exchange network. The system calculates the total local heat supply and demand of the flotation cell and the target reaction temperature based on flow rate, heat exchanger efficiency, valve opening, and virtual reaction temperature field. The output is a standardized optimized heat exchange route instruction set for branch matching, flow distribution, and target valve opening. The solution employs an improved particle swarm optimization algorithm with chaotic initialization and adaptive inertia weighting. The particle number is set to 80, the maximum iteration to 200, and the inertia weight to 0.4 to 0.9. The solution process sequentially generates an initial population through chaotic initialization, calculates fitness, adds a penalty factor of 100 to constraint violations, and adaptively updates particle velocity and position. Convergence is determined when 200 iterations are completed or the change in the optimal solution is less than 100%. The steps for demapping and generating control commands are as follows: the algorithm implementation parameters are a temperature deviation penalty coefficient of 50 yuan / ℃・h, an execution cycle of ≤200ms, and linkage with the control system through the Modbus TCP / IP protocol to form a solution-execution-feedback closed loop. When the equipment fails, the faulty unit is automatically shielded and a fault-free constraint optimization solution is regenerated, while an alarm command is output.

[0059] Based on the optimized heat exchange routing instructions, the heat exchange network is dynamically reconstructed. The purpose is to materialize the theoretically optimal solution generated by the algorithm into actual fluid paths and heat exchange behaviors. The control system decomposes the received optimized heat exchange routing instructions into specific action instructions for a series of electrically controlled valves in the network. For example, the above instruction will be translated into "open valve A3 to 30% opening, simultaneously fully open valve B3, and adjust the main pipeline valves accordingly to maintain total flow balance." By precisely adjusting the valve openings of each heat exchange branch through the PID controller, the fluid path in the network is changed, thereby efficiently and directionally transporting heat from the specified heat-rich fluid, such as the exothermic mineral slurry or equipment cooling water, to the heat-demanding fluid, such as the cold mineral slurry entering the workshop in winter or the reagents that need to be heated, achieving dynamic heat balance throughout the entire system.

[0060] For example, the control system periodically acquires the state of the process fluid in the cascaded heat exchange network, and measures the current flow rate of the high-temperature tailings slurry in channel A as 50 using sensors deployed at each node. The temperature was 55℃, and the flow rate of the low-temperature grinding slurry in channel B was measured to be 120. The temperature is 15℃. The system calls an improved particle swarm optimization mixed-integer nonlinear programming algorithm adapted to the lithium mica flotation process, taking the real-time temperature and flow rate as well as the global heat demand fed back from the virtual reaction temperature field as inputs, to minimize the objective function that includes external energy consumption costs, fluid transport energy consumption costs, and temperature deviation penalty costs. The solution is then calculated. During algorithm execution, the number of particles is set to 80 and the maximum number of iterations to 200. A temperature deviation penalty coefficient of 50 yuan / ℃·h is set. After iterative calculations involving chaotic initialization and adaptive weights, when the change in the optimal solution is less than... The system converges and generates an optimized heat exchange route instruction. This instruction directs 30% of the flow rate of the high-temperature fluid in route A to heat exchanger No. 3. Based on this, the system dynamically adjusts the opening of the electric regulating valves of the corresponding heat exchange branches in the cascaded heat exchange network. The PID controller precisely adjusts the valves to the values ​​required by the instruction, thereby directionally transferring heat from the heat-rich tailings slurry to the heat-requiring grinding slurry, achieving dynamic heat balance throughout the entire system.

[0061] Optionally, the method further includes: After the completion of the lithium mica flotation production batch, obtain the final flotation index of that production batch; The final flotation index is correlated with the historical virtual reaction temperature field data of the production batch during the production process to generate model optimization results. Based on the optimization results of the model, the internal parameters of the thermodynamic-fluid dynamics model are adjusted.

[0062] Specifically, after each batch of lepidolite flotation production is completed, data collection and archiving of the final flotation parameters are performed. These final flotation parameters are key performance parameters for evaluating the success of the flotation process, primarily including the grade of the lithium concentrate. The system measures the mass fraction of the concentrate and the lithium recovery rate. These metrics are typically obtained through sampling and testing of the final concentrate and tailings products, and are discrete values ​​that can only be determined after a batch has ended. The system binds these metrics to a unique identifier for that production batch and stores them in the production history database.

[0063] The purpose of performing historical data correlation analysis is to establish a quantitative relationship between the virtual reaction temperature field state during the production process and the final flotation results. The system retrieves all historical virtual reaction temperature field data for a specified production batch throughout the entire production cycle from the database; this data constitutes a large four-dimensional dataset. The system uses data mining algorithms, such as principal component analysis or partial least squares, to extract key statistical features from this temperature field data, such as average temperature, temperature variance, duration of high-temperature or low-temperature zones, and volume percentage. Then, these features are compared with the final flotation indicators of the batch using multiple regression analysis or fitted with machine learning models to quantify the impact of specific temperature field distribution patterns on flotation grade and recovery rate.

[0064] Based on the results of correlation analysis, adaptive adjustments are made to the model parameters. The aim is to solidify the "experience" learned from production data into the thermodynamic-fluid dynamics model, thereby improving its predictive ability. Correlation analysis generates model optimization results, clearly indicating which operating conditions the current model parameters are predicting, resulting in temperature fields with low correlation to the final outcome. For example, the analysis might reveal that the model underestimates the exothermic reaction intensity under high-concentration collectors. Based on this optimization result, key parameters within the model are automatically adjusted using optimization algorithms, such as particle swarm optimization or genetic algorithms. These parameters may include, but are not limited to, the kinetic constants of the collector reaction, the correction coefficient for the enthalpy of reaction, or empirical constants in the turbulence model. The goal of parameter adjustment is to minimize the following objective function. : , In the formula, This indicates the summation of data from multiple historical batches. and The predicted grade and recovery rate are obtained by resimulating historical operating conditions using an adjusted model. and These are the actual measured grades and recovery rates; and These are weighting coefficients used to balance the importance of grade and recovery. They are solved iteratively to... By minimizing the parameter set, the system completes the calibration and optimization of the thermo-hydrodynamic model, enabling it to provide more accurate virtual reaction temperature field predictions in the next production batch, forming a continuously improving closed-loop learning system.

[0065] For example, after a batch of lithium mica flotation production is completed, the control system extracts the measured final flotation parameters of that batch from the database, including the lithium concentrate grade. The lithium recovery rate was 4.2%. The percentage was 85.5%. The system synchronously retrieved historical virtual reaction temperature field data during the operation of this batch, extracted characteristic quantities using partial least squares method, and performed correlation analysis with the indicators. To optimize the prediction accuracy of the thermodynamic-fluid dynamics model, the system set a grade weighting coefficient. The recovery rate weighting factor is 0.6. The value is 0.4. The predicted grade for this operating condition by the model before adjustment is known. The predicted recovery rate is 3.8%. The value is 82.0%. The system is substituted into the objective function formula. The system then invokes an improved particle swarm optimization algorithm, automatically correcting the model's internal kinetic constants and reaction enthalpy correction coefficients during 200 iterations, until... Approaching 4.2% and Approaching 85.5%, making The value dropped to The system then adjusts the model's internal parameters based on the optimization results to complete the closed-loop calibration of the prediction model.

[0066] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a precise control system for the reaction temperature of a lithium mica flotation collector, the system comprising: The process parameter acquisition module is used to acquire real-time process parameters, including measured temperature at different locations, collector addition flow rate, and slurry flow rate. The virtual reaction temperature field generation module is used to process the real-time process parameters and generate a virtual reaction temperature field that characterizes the reaction state of the collector on the surface of mineral particles. The temperature regulation decision module is used to compare the virtual reaction temperature field with the preset target reaction temperature and generate a temperature regulation decision. The collector micro-reaction addition unit control module is used to adjust the addition parameters of the corresponding collector micro-reaction addition unit according to the local temperature deviation indicated in the temperature regulation decision, and to perform temperature compensation by changing the heat release or absorption of the collector reaction in the local area. The cascaded heat exchange network control module is used to synchronously adjust the heat exchange routes between process fluids in the cascaded heat exchange network and dynamically balance the overall heat of the system.

[0067] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0068] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for precisely controlling the reaction temperature of a lithium mica flotation collector, characterized in that, The method includes: Real-time process parameters are obtained, including measured temperature at different locations, collector addition flow rate, and slurry flow rate. The real-time process parameters are processed to generate a virtual reaction temperature field characterizing the reaction state of the collector on the surface of mineral particles; The virtual reaction temperature field is compared with a preset target reaction temperature to generate a temperature regulation decision. Based on the local temperature deviation indicated in the temperature regulation decision, the addition parameters of the corresponding collector micro-reaction addition unit are adjusted, and temperature compensation is performed by changing the heat release or absorption of the collector reaction in the local area. Synchronously adjust the heat exchange routes between process fluids in the cascaded heat exchange network to dynamically balance the overall heat of the system.

2. The method for precisely controlling the reaction temperature of a lithium mica flotation collector according to claim 1, characterized in that, The virtual reaction temperature field generated to characterize the reaction state of the collector on the surface of mineral particles includes: Obtain ore characteristic parameters for the physicochemical properties of the ore; The real-time process parameters are fused with the ore characteristic parameters to form a comprehensive process state vector; The integrated process state vector is input into a preset thermodynamic-fluid dynamics model for calculation, and a virtual reaction temperature field containing spatial temperature distribution information of the reaction zone within the flotation cell is output.

3. The method for precisely controlling the reaction temperature of a lithium mica flotation collector according to claim 1, characterized in that, The adjustment of the addition parameters of the corresponding collector micro-reaction addition unit includes: Based on the temperature regulation decision, a first local region indicating a temperature below the target reaction temperature is identified; Based on the mapping relationship between the three-dimensional space within the flotation cell and the execution unit, the target micro-reaction addition unit serving the first local region is determined; Based on the compensation amount required by the temperature regulation decision, a first control command is generated and sent to the target micro-reaction addition unit to increase its collector addition concentration or preheat the added collector, thereby heating the first local area in situ through enhanced chemical reaction heat.

4. The method for precisely controlling the reaction temperature of a lithium mica flotation collector according to claim 1, characterized in that, The adjustment of the addition parameters of the corresponding collector micro-reaction addition unit also includes: Based on the temperature regulation decision, a second local region indicating a temperature higher than the target reaction temperature is identified; Based on the mapping relationship between the three-dimensional space within the flotation cell and the execution unit, the corresponding target micro-reaction addition unit serving the second local region is determined; Based on the compensation amount required by the temperature regulation decision, a second control command is generated and sent to the corresponding target micro-reaction addition unit to reduce its collector addition concentration or cool the added collector, thereby reducing the heat of chemical reaction and performing in-situ cooling of the second local area.

5. The method for precisely controlling the reaction temperature of a lithium mica flotation collector according to claim 1, characterized in that, The heat exchange routes between process fluids in the synchronously regulated cascaded heat exchange network include: Obtain the current temperature and flow rate of each process fluid in the cascaded heat exchange network; Based on the virtual reaction temperature field and the current temperature and flow rate of each process fluid, an optimized heat exchange route instruction is generated by calculating using a preset energy routing optimization algorithm. According to the optimized heat exchange routing instructions, the valve opening of each heat exchange branch in the cascaded heat exchange network is dynamically adjusted to directionally transport heat from the heat-rich fluid to the heat-demanding fluid.

6. The method for precisely controlling the reaction temperature of a lithium mica flotation collector according to claim 2, characterized in that, The method further includes: After the completion of the lithium mica flotation production batch, obtain the final flotation index of that production batch; The final flotation index is correlated with the historical virtual reaction temperature field data of the production batch during the production process to generate model optimization results. Based on the optimization results of the model, the internal parameters of the thermodynamic-fluid dynamics model are adjusted.

7. The method for precisely controlling the reaction temperature of a lepidolite flotation collector according to claim 1, characterized in that, The step of comparing the virtual reaction temperature field with a preset target reaction temperature to generate a temperature regulation decision includes: The temperature of each grid node in the virtual reaction temperature field is compared with the target reaction temperature point by point, and the temperature deviation value of each node is calculated. Based on the temperature deviation value and the preset temperature deviation threshold, a first local region below the target reaction temperature and a second local region above the target reaction temperature are identified, and the spatial location and deviation amount of each region are determined. Based on the type, spatial location, and deviation of the first and second local regions, a temperature regulation decision containing regional attributes and compensation amounts is generated.

8. The method for precisely controlling the reaction temperature of a lithium mica flotation collector according to claim 1, characterized in that, The generation temperature regulation decision also includes: If there is an overall temperature deviation in the temperature regulation decision indication, an external energy regulation command is generated; Adjust the input power of the external heat source or external cold source according to the external energy adjustment command.

9. The method for precisely controlling the reaction temperature of a lithium mica flotation collector according to claim 1, characterized in that, The acquisition of real-time process parameters includes: The measured temperature, collector addition flow rate, and slurry flow rate were collected at different locations at each point; The collected raw data is denoised, filtered, and time-synchronized to form standardized real-time process parameters.

10. A precise control system for the reaction temperature of a lepidolite flotation collector, applied to a precise control method for the reaction temperature of a lepidolite flotation collector as described in any one of claims 1-9, characterized in that, The system includes: The process parameter acquisition module is used to acquire real-time process parameters, including measured temperature at different locations, collector addition flow rate, and slurry flow rate. The virtual reaction temperature field generation module is used to process the real-time process parameters and generate a virtual reaction temperature field that characterizes the reaction state of the collector on the surface of mineral particles. The temperature regulation decision module is used to compare the virtual reaction temperature field with the preset target reaction temperature and generate a temperature regulation decision. The collector micro-reaction addition unit control module is used to adjust the addition parameters of the corresponding collector micro-reaction addition unit according to the local temperature deviation indicated in the temperature regulation decision, and to perform temperature compensation by changing the heat release or absorption of the collector reaction in the local area. The cascaded heat exchange network control module is used to synchronously adjust the heat exchange routes between process fluids in the cascaded heat exchange network and dynamically balance the overall heat of the system.

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